arXiv AI
Jun 30
Learning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution
arXiv:2606. 29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled.
Human Vision Constrained Super-Resolution
arXiv:2411. 17513v3 Announce Type: replace-cross Abstract: Modern deep-learning super-resolution (SR) techniques process images and videos independently of the underlying content and viewing conditions.
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arXiv Computer Vision
Aug 26
Implicit Neural Representation-Based Continuous Single Image Super-Resolution: An Empirical Benchmark
arXiv:2601.17723v3 Announce Type: replace Abstract: Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR). However, no systematic emp...
arXiv Computer Vision
Sep 1
PixelIR: Fidelity-Perception Decoupling via Pixel-Space Image-Residual Flow Matching for Efficient One-Step Real-World Super-Resolution
arXiv:2608.30782v1 Announce Type: new Abstract: Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realis...
arXiv Computer Vision
Sep 16
CineScale: Tuning-Free High-Resolution Video Generation
arXiv:2508.15774v2 Announce Type: replace Abstract: Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data...
arXiv Computer Vision
Aug 25
FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
arXiv:2608.23549v1 Announce Type: new Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
arXiv AI
Jul 20
Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution
arXiv:2607. 15711v1 Announce Type: cross Abstract: Diffusion-based methods have achieved impressive performance in real-world image super-resolution (Real-ISR) by leveraging large pre-trained stable diffusion (SD) models as powerful generative priors.
arXiv Computer Vision
Sep 4
SPARK: Input-Conditioned Sparse Activation Modulation for Frozen DiT-based Super-Resolution
The paper introduces SPARK, a lightweight input‑conditioned controller that modulates only a few dominant channels in frozen Diffusion Transformer (DiT) based super‑resolution models. By predicting bounded per‑channel affine transformations for selected channels, SPARK improves reconstruction fidelity and perceptual quality without fine‑tuning the backbone or adding adapters. Experiments on three DiT‑based SR backbones across DIV2K, RealSR, and DRealSR demonstrate consistent gains while modulating only eight channels per stream and block.
arXiv Machine Learning
Aug 21
Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning
arXiv:2605. 10546v2 Announce Type: replace Abstract: Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded in principled design.
arXiv Computer Vision
Sep 15
DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution
arXiv:2609.15120v1 Announce Type: new Abstract: Benefiting from the powerful generative priors of diffusion models, diffusion-based real-world image super-resolution (Real-ISR) methods have demonstra...
Hugging Face Trending Papers
Aug 24
FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces artifacts when input views are sparse or target v...
arXiv AI
Sep 10
Adaptive Densification for High-Fidelity and Efficient Sparse Gaussian Splatting in Arbitrary-Scale Super-Resolution
The paper introduces QuADA-GS, a method for Arbitrary-Scale Super-Resolution that dynamically densifies 2D Gaussian splatting based on low‑resolution input. By allocating Gaussians adaptively to structurally complex regions and employing a sparse communication mechanism, it balances high visual fidelity with lower computational cost. Experiments show that this approach achieves a competitive trade‑off between quality and efficiency for super‑resolution tasks.
arXiv Machine Learning
Jul 7
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
arXiv:2404. 06294v2 Announce Type: replace-cross Abstract: Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart.